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fix range bug in TreeCompress, update parser to autofail non-singular classifications

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Arthur Lu
2025-06-11 19:10:19 +00:00
parent 7bee40ecf9
commit 6de3807fe2
4 changed files with 49 additions and 109 deletions
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142
CompressedTreeParser.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 1,
"id": "938dec51",
"metadata": {},
"outputs": [],
@@ -17,102 +17,37 @@
"from matplotlib import pyplot as plt\n",
"from labels import mac_to_label\n",
"import json\n",
"import math\n"
"import math"
]
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 2,
"id": "442624c7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[6, 40234, 5228], [6, 40234, 5228], [6, 443, 46330], [6, 3063, 443], [1, 0, 0], [17, 61725, 53], [6, 5228, 40234], [6, 443, 3063], [0, 0, 0], [0, 0, 0], [6, 40234, 5228], [17, 4500, 45966], [17, 53, 61725], [1, 0, 0], [6, 46330, 443], [6, 443, 46330], [0, 0, 0], [1, 0, 0], [6, 3063, 443], [6, 443, 3063]]\n"
]
}
],
"outputs": [],
"source": [
"Set1 = pd.read_csv('data.csv').values.tolist()\n",
"X = [i[0:3] for i in Set1]\n",
"Y =[i[3] for i in Set1]\n",
"\n",
"print(X[0:20])\n"
"Y =[i[3] for i in Set1]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f18850b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Amazon Echo', 'Belkin Motion Sensor', 'Belkin Switch', 'Blipcare BP Meter', 'Dropcam', 'HP Printer', 'NEST Smoke Sensor', 'Netatmo Camera', 'Netatmo Weather station', 'Pixstart photo frame', 'Samsung Smart Cam', 'Smart Things', 'TP-Link Camera', 'TP-Link Plug', 'Triby Speaker', 'Withings', 'Withings Scale', 'other']\n"
]
}
],
"source": [
"classes = [\n",
" \"Amazon Echo\",\n",
" \"Belkin Motion Sensor\",\n",
" \"Belkin Switch\",\n",
" \"Blipcare BP Meter\",\n",
" \"Dropcam\",\n",
" \"HP Printer\",\n",
" \"NEST Smoke Sensor\",\n",
" \"Netatmo Camera\",\n",
" \"Netatmo Weather station\",\n",
" \"Pixstart photo frame\",\n",
" \"Samsung Smart Cam\",\n",
" \"Smart Things\",\n",
" \"TP-Link Camera\",\n",
" \"TP-Link Plug\",\n",
" \"Triby Speaker\",\n",
" \"Withings\",\n",
" \"Withings Scale\",\n",
" \"other\"\n",
" ]\n",
"\n",
"print(classes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"id": "12ad454d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1, 11, 17], [1, 11, 17], [1, 4, 11, 15, 17], [17], [8, 17], [], [1, 11, 17, 15], [11], [8, 17], [8, 17], [1, 11, 17], [], [], [8, 17], [17]]\n",
"['other', 'other', 'Dropcam', 'other', 'Netatmo Camera', 'other', 'Triby Speaker', 'Smart Things', 'other', 'Belkin Switch', 'other', 'Netatmo Camera', 'Netatmo Camera', 'other', 'other']\n",
"947072\n",
"947072\n",
"The accuracy was: 0.0\n"
]
}
],
"outputs": [],
"source": [
"predict_Yt = []\n",
"with open('compressed_tree.json', 'r') as file:\n",
" data = json.load(file)\n",
" #print(data['layers']['dst'])\n",
" classes = data[\"classes\"]\n",
" for x in X:\n",
" counter = 0\n",
" class_set = []\n",
" for feature in reversed(data['layers']): #Have to reverse this list due to structure of the data.csv file and how it aligns with the compressed tree layers\n",
" #print(\"The feature is: \", feature)\n",
"\n",
" for node in data['layers'][feature]:\n",
" #print(\"The node is: \", node)\n",
" #print(f\"The min is: {node['min']}, the max is {node['max']}\")\n",
" if node['min'] is None:\n",
" if x[counter] < node['max']:\n",
" class_set.append(node['classes'])\n",
@@ -130,39 +65,42 @@
" break #is this an issue?\n",
"\n",
" counter += 1\n",
" #print(\"The list of classes is: \", class_set)\n",
" result = set(class_set[0])\n",
" for s in class_set[1:]:\n",
" result.intersection_update(s)\n",
"\n",
" #print(\"The result was: \", result)\n",
" predict_Yt.append(list(result))\n",
" #print(predict_Yt)\n",
" #predict_Yt.append(list(result))\n",
" #print(result)\n",
" if len(result) == 1:\n",
" prediction = list(result)[0]\n",
" pred_class = classes[prediction]\n",
" predict_Yt.append(pred_class)\n",
" else:\n",
" predict_Yt.append(None)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8b4c56b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8448217242194891\n"
]
}
],
"source": [
"correct = 0\n",
"for i in range(len(Y)):\n",
" prediction = predict_Yt[i]\n",
" if prediction != None and Y[i] == prediction:\n",
" correct += 1\n",
"\n",
" \n",
"\n",
"print(predict_Yt[0:15])\n",
"print(Y[0:15])\n",
"\n",
"\n",
"print(len(predict_Yt))\n",
"print(len(Y))\n",
"\n",
"\n",
"counter = 0\n",
"for index in range(len(Y)):\n",
" for possible_class_index in predict_Yt[index]:\n",
" if Y[index] == classes[possible_class_index]:\n",
" counter += 1\n",
"\n",
"\n",
"print(\"The accuracy was: \", counter / len(Y))\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
"print(correct / len(Y))"
]
}
],
@@ -182,7 +120,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.12.7"
}
},
"nbformat": 4,